Everyday across the world, business leaders are making trade-offs, determining how to invest the limited resources of their organizations to grow their businesses and create greater value. These leaders are making complex decisions about what products and services to offer, whether to expand their work forces or invest in new machinery or equipment, whether to hire more sales people or more engineers, whether to borrow money or sell shares of the company, and so forth. In making these decisions, business leaders, as well as the investors (including debt holders) who back them, often look for outside guidance in the form of industry classifications that help them identify related businesses for purposes of understanding best practices and performance benchmarking.
One popular classification system, which presently classifies over 38,000 publicly traded companies based on their principal business activity, is the Global Industry Classification Standard (GICS), a four-tiered system of 11 sectors, 24 industry groups, 68 industries, and 157 sub-industries. Other systems, which can be used along with GICS, classify businesses based on their market capitalization (that is, the aggregate market value of their publicly traded stock), placing them into one of three categories: small cap, mid cap, and large cap. Another complementary system subjectively classifies businesses, more precisely their stocks, as growth, value, or income stocks, based on expectations for growth in stock price, perceived undervaluation of the stock in the market, or expected dividend income using accounting information.
The present inventors have recognized at least three problems with these industry and accounting-based classification systems. First, traditional industrial classification systems, like GICS (Global Industry Classification Standard), fail to recognize the blurring lines between industries and present overly simplistic views of many businesses. For example, Apple, maker of the iPhone smartphone and MacBook computers, is classified only within the GICS Information Technology sector, though it has significant activity within the telecommunication-services sector with its Facetime and iMessage services and within the consumer discretionary sector with its iTunes media platform. Google and Amazon likewise stretch across multiple classes, but are only classified in one. And network-based companies, like Facebook and LinkedIn, find it challenging to find any fitting industrial category at all.
Second, accounting-based key performance indicators, such as those based on market capitalization, revenue growth, expenses, or earnings, have limited value in identifying likely winning businesses in our digitally driven, information economy. In particular, conventional business valuation techniques are premised on book value (the difference between total assets and total liabilities of a business) and net cash flow. However, these accounting definitions of business value, sanctioned by Generally Accepted Accounting Principles (GAAP), treat financial and tangible assets (e.g., things and money) as the primary assets affecting business worth and future performance. Such accounting definitions of business value largely ignore the increasing relevance and value of intangible assets such as insights, intellect, data, and relationships, in determining market valuations. Studies, for example by the Brookings Institute and Ocean Tomo, have shown that since the 1970s, corporate tangible assets have been making a decreasing contribution to the total market value of publicly traded technology businesses, shrinking from 80% to less than 20% today. (See also Patent Cooperation Treaty Application Publication No. WO2000/034911 by Barry Libert et al. which further describes the widespread undercounting of intangibles.)
Third, industry and accounting-based classifications divide the business world into various data silos that make it difficult for business leaders and investors to see that many successful and unsuccessful businesses of various sizes countries, continents, industries, size, scale, etc. operate similarly in how they allocate their capital resources. These allocation patterns ultimately show up as successful or failed business models, offering valuable insights and clues to success for those who can see them. Moreover, even when a business model surfaces for public study, there are no common frameworks or methods for systematically comparing and contrasting it with others. This ultimately limits the ability of business leaders and investors to learn from the successes and failures of others.
Accordingly, the present inventors have identified a need for new ways of classifying businesses that transcend conventional industry categories, that provide deeper insight into what separates massively successful organizations from mediocre ones, and that facilitate understanding of successful and unsuccessful business models.